Tag Archives: NINO3.4

“Droughts and flooding rains” *

The climates of places in Australia cycle from hot, arid and dry, to cold, humid and wet every couple of years. (Dorothea Mackellar said she loved a sunburnt country “of droughts and flooding rains”. *) This is a kind of quasi-biennial oscillation (QBO). For more about the QBO, see this post, and the links in it. The cycles get weaker and stronger, more droughty or more rainy, and sometimes take about one year, sometimes three or more.

The climate of the Pacific Ocean has similar cycles, called the Southern Oscillation, discovered by Gilbert Walker a century ago. The pressure difference between Darwin and Tahiti oscillates in a way that reflects other widespread changes in climate. This is now called the El Niño – Southern Oscillation (ENSO) and it is monitored by sea-surface temperature around Nauru in the Pacific Ocean, called NINO3.4. Now that we have up-to-date data on NINO3.4, the public has been led to believe that the data can be used to forecast Australian weather. It really can’t.

Problem No.1: Weather varies from place to place.

Every district in Australia has different weather, so one size does not fit all. Wasyl Drosdowsky made a map defining the regions that have consistent relationships to ENSO and other indices, but nobody has taken up the idea. (I would if I was boss of the Bureau of Meteorology!) Drosdowsky’s regions are rather similar to the States, but Victoria and the southern half of South Australia form a single region.

Problem No.2: Forecast is too late.

The ENSO cycle does not predict a cycle in any part of Australia because it happens at about the same time, and it takes a month or more to collate the data. Weather prediction from ENSO is always late. Consequently, there is a business to predict ENSO some months ahead. These predictions are very unreliable. Then the predictions of ENSO values are used to predict Australian weather, with vague statements of which regions will be affected.

[Note added 14/07/2015. Updated graphs comparing the ENSO log from 1999 to 2014 with smoothed daily maximum temperature anomaly and smoothed monthly rainfall anomaly at Manilla are in this post. Manilla’s climate has not related very well to ENSO since mid-2011.]

The graphs above are like those in two previous posts, but show how Manilla smoothed monthly dew point anomalies, like temperature anomalies and rainfall anomalies, relate to the El Niño-Southern Oscillation (ENSO).

High (El Niño) values of Sea Surface Temperature (NINO3.4) are shown here to relate to low humidity at Manilla, NSW. As humidity data, I estimate dew points daily at sunrise. Dew points, like Sea Surface Temperatures, are expressed in degrees celsius, but corresponding anomalies take the opposite sense. The first graph plots the Manilla dew point anomaly, given a negative sign, and the NINO3.4 anomaly. To improve the match, I have lagged the Manilla dew points by three months. As an example, I have noted on the graph the match of Manilla’s November 2005 humidity peak with the La Nina ENSO peak of February 2006.

To the eye, the over-all match is better than in either the rainfall or the maximum temperature plots of earlier posts. The two curves here match very well from 2000 to 2007.

The second graph shows the discrepancy between the two curves. Dashed lines show limits of a good match at +/-0.5 degrees. The nature of each larger discrepancy is noted. (“Here” in text boxes means “at Manilla”.)
After 2007 there are large mis-matches between Manilla dew point and ENSO. Dew point fluctuations suddenly become less than might be expected from NINO3.4 values. It may be relevant that, as I postedelsewhere in July 2010, skies suddenly became very much cloudier at Manilla after August 2007.

The three sets of graphs show “teleconnections” between Sea Surface Temperatures in the equatorial Pacific and climate variables at Manilla in inland NSW, Australia. Climatic peaks come earlier at Manilla than in the Pacific:

In a simple-minded way, it seems to me more likely that Australia’s climate drives the Southern Oscillation than the other way around. I know that this is speculation. (Sort of like Abraham Ortellius suggesting in 1587 that Africa and South America might have drifted apart.)

Notes1. High frequency noise is reduced in the case of the Manilla monthly data by a Gaussian smoothing function of half-width six months.2. On advice, I represent the El Nino – Southern Oscillation phenomenon (ENSO) by the NINO3.4 area anomalies from the OISSTv2 data set.My enquiries about the best data to use are in this “weatherzone” thread.The ensemble of sea surface temperatures does not have much high-frequency noise. There is some, however, and I have used the same smoothing as used in the (formerly authoritative) Oceanic Nino Index (ONI), that is, a running mean of each three monthly values.

This was posted originally in a “weatherzone” forum, with the date 12 November 2011. It is posted here with the nominal date 29 November 2011.

The graphs above are like those in an earlier post, but show how Manilla monthly rainfall anomalies, rather than maximum temperature anomalies relate to the El Nino-Southern Oscillation (ENSO). Most people using ENSO want to predict Australian regional rainfall.

In the second graph I have improved the match at peaks and troughs of smoothed Manilla monthly rainfall anomalies and NINO3.4 sea surface temperature anomaly data in two ways.
1. I converted the sea surface temperature anomaly (degrees C) into a model of resultant rainfall anomaly (mm) by multiplying by minus fifteen.
2. I added 3.7 mm of rainfall to the Manilla figures, and I lagged the data by two months.

To the eye, the over-all correspondence between actual and modelled rainfall is good, but not quite as good as in the temperature graphs. One form of mis-match is that two of the greatest rainfall deficits (“El Nino” Nov-06, Dec-09) are broader and shallower than in the model. (Perhaps an arithmetic measure of rainfall anomaly is not the best.)

The third graph shows how much Manilla rainfall, as adjusted, differs from the rainfall “predicted” by the NINO3.4 model. Dashed lines show limits of a good match at +/- 7.5 mm (corresponding to +/-0.5 degrees). The nature of each larger discrepancy is noted.

A good match demands lagging actual rainfall at Manilla by two months. That implies that peaks and troughs in Manilla rainfall anomalies happen two months before the matching anomalies of NINO3.4. I wonder if prediction is even practical if that is the case in other parts of Australia.

Notes1. High frequency noise is reduced in the case of the Manilla monthly data by a Gaussian smoothing function of half-width six months.2. On advice, I represent the El Nino – Southern Oscillation phenomenon (ENSO) by the NINO3.4 area anomalies from the OISSTv2 data set.My enquiries about the best data to use are in this “weatherzone” thread.The ensemble of sea surface temperatures does not have much high-frequency noise. There is some, however, and I have used the same smoothing as used in the (formerly authoritative) Oceanic Nino Index (ONI), that is, a running mean of each three monthly values.

This was posted originally in a “weatherzone” forum, with the date 28 October 2011. It is posted here with the nominal date 16 November 2011.

[Note added:
This post relating ENSO to Manilla temperature is matched by similar posts relating ENSO to Manilla rainfalland to Manilla humidity (dew point). Manilla climate peaks and troughs generally happen before the related ENSO peaks and troughs, not after them.]

Smoothed daily maximum temperature anomalies for 140 months at Manilla, NSW are compared with NINO3.4 region Sea Surface Temperature anomalies. They match very closely, especially at peaks and troughs of the Southern Oscillation. The first graph is a log of the data as described in the notes below.
The match can be improved, as in the second graph, by making two adjustments. The reference periods for the anomalies are not the same. In any case it is pure coincidence that the temperature values are so close. I have chosen to add 0.2 degrees to the Manilla figures. At several of the major peaks and troughs the Manilla temperature leads the Sea Surface temperature by one month. I have chosen to lag all the Manilla temperatures by one month.
The third graph quantifies the remaining discrepancies. For most of this short record, the adjusted, one-month lagged Manilla smoothed daily maximum temperatures agreed with ENSO3.4 Sea Surface Temperatures within a margin of 0.5 degrees. Periods when the discrepancy was greater are noted on the graph.
At first (Sep-99 to Nov-00: 15 months) Manilla temperatures were in phase with the Southern Oscillation but one degree warmer.
For a time (Dec-00 to Dec-01: 13 months) there was no agreement.
From Jan-02 to Jun-03 (18 months) temperatures agreed.
From Jul-03 to May-06 (35 months) there was again no agreement.
In the long period (59 months) from Jun-06 to the end of the record in Apr-11, temperatures agreed except for one interruption: Manilla temperature lagged by three months at the La Nina trough of Feb-08, causing a discrepancy of minus one degrees.
In the 140-month record, Manilla temperatures faithfully followed Sea Surface temperatures in 77 months (55%), and were in phase in another 15 months (11%). Times when there were large discrepancies were generally times when the Southern Oscillation was near-neutral.

Notes1. High frequency noise is reduced in the case of the Manilla monthly data by a gaussian smoothing function of half-width six months.2. On advice, I represent the El Nino – Southern Oscillation phenomenon (ENSO) by the NINO3.4 area anomalies from the OISSTv2 data set.My enquiries about the best data to use are in this “weatherzone” thread.The ensemble of sea surface temperatures does not have much high-frequency noise. There is some, however, and I have used the same smoothing as used in the (formerly authoritative) Oceanic Nino Index (ONI), that is, a running mean of each three monthly values.

This was posted originally in a “weatherzone” forum, with the date 25 October 2011. It is posted here with the nominal date 28 October 2011, and made “sticky” on 27 May 2014.

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Monthly and seasonal weather reports for Manilla now extend back more than ten years to June 2007. [Select ARCHIVES for the month following.]
"Manilla 3-year climate trends" graphs and reports extend back to May 2010 as consecutive months. Earlier graphs and reports are accessed in ARCHIVES September 2002, 2004, 2006, and 2008.